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dc.contributor.authorSathiyakumari, K-
dc.contributor.authorVijaya, M S-
dc.date.accessioned2023-11-22T03:47:47Z-
dc.date.available2023-11-22T03:47:47Z-
dc.date.issued2018-
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-981-10-5544-7_12-
dc.description.abstractSocial networks have obtained masses hobby recently, largely because of the success of online social networking Web sites and media sharing sites. In such networks, rigorous and complex interactions occur among several unique entities, leading to huge information networks with first rate commercial enterprise ability. Network detection is an unmanaged getting to know challenge that determines the community groups based on common place hobbies, career, modules, and their hierarchical agency, the usage of the records encoded in the graph topology. Locating groups from social network is a tough mission because of its topology and overlapping of various communities. In this research, edge betweenness modularity and random walks is used for detecting groups in networks with node attributes. The twitter data of the famous cricket player is used here and network of friends and followers is analyzed using two algorithms based on edge betweenness and random walks. Also the strength of extracted communities is evaluated using on modularity score and the experiment results confirmed that the cricket player’s network is dense.en_US
dc.language.isoen_USen_US
dc.publisherSpringer Linken_US
dc.subjectEdge betweennessen_US
dc.subjectRandom walksen_US
dc.subjectModularityen_US
dc.subjectCommunity detectionen_US
dc.subjectSocial networken_US
dc.titleIDENTIFICATION OF SUBGROUPS IN A DIRECTED SOCIAL NETWORK USING EDGE BETWEENNESS AND RANDOM WALKSen_US
dc.typeOtheren_US
Appears in Collections:4.Conference Paper (09)

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